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Classification of Tissue Regions in Histopathological Images: Comparison Between Pre-trained Convolutional Neural Networks and Local Binary Patterns Variants

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Deep Learners and Deep Learner Descriptors for Medical Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 186))

Abstract

The identification of tissue regions within histopathological images represents a fundamental step for diagnosis, patient stratification and follow-up. However, the huge amount of image data made available by the ever improving whole-slide imaging devices gives rise to a bottleneck in manual, microscopy-based evaluation. Furthermore, manual procedures generally show a significant intra- and/or inter-observer variability. In this scenario the objective of this chapter is to investigate the effectiveness of image features from last-generation, pre-trained convolutional networks against variants of Local Binary Patterns for classifying tissue sub-regions into meaningful classes such as epithelium, stroma, lymphocytes and necrosis. Experimenting with seven datasets of histopathological images we show that both classes of methods can be quite effective for the task, but with a noticeable superiority of descriptors based on convolutional neural networks. In particular, we show that these can be seamlessly integrated with standard classifiers (e.g. Support Vector Machines) to attain overall discrimination accuracy between 95 and 99%.

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Notes

  1. 1.

    Underline indicates colour descriptors. For a detailed description of each method please refer to the given references.

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Acknowledgements

This work was partially supported by the Italian Ministry of University and Research (MIUR) under the Individual Funding Scheme for Fundamental Research ‘FFABR’ 2017 (F. Bianconi) and by the Department of Engineering at the Università degli Studi di Perugia, Italy, within the Fundamental Research Grants Scheme 2018 (F. Bianconi).

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Authors and Affiliations

  1. Department of Medicine III, University Hospital RWTH, Pauwelsstraße 30, 52074, Aachen, Germany

    Jakob N. Kather

  2. Department of Engineering, Università degli studi di Perugia, Via Goffredo Duranti 93, 06125, Perugia, Italy

    Raquel Bello-Cerezo, Francesco Di Maria & Francesco Bianconi

  3. National Center for Tumour Diseases, Universität Heidelberg, Neuenheimer Feld 460, 69120, Heidelberg, Germany

    Niels Halama

  4. Department of Surgery, Leiden University Medical Center, Albinusdreef 2, 2333, ZA Leiden, The Netherlands

    Gabi W. van Pelt & Wilma E. Mesker

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  1. Jakob N. Kather
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Correspondence to Francesco Bianconi .

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  1. Department of Information Engineering, University of Padova, Padova, Italy

    Loris Nanni

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

  3. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO, USA

    Rick Brattin

  4. Department of Information Engineering, Intelligent Autonomous Systems Lab, Padova, Padova, Italy

    Stefano Ghidoni

  5. University of Technology, Sydney, Australia

    Lakhmi C. Jain

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Kather, J.N. et al. (2020). Classification of Tissue Regions in Histopathological Images: Comparison Between Pre-trained Convolutional Neural Networks and Local Binary Patterns Variants. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-42750-4_3

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